On the implementation of Cognitive Autonomous Networks

Cognitive Autonomous Networks (CAN) is a promising approach for advancing network management automation using machine learning based functions called Cognitive Functions (CFs). Thereby, the CFs interact with the environment to learn and decide suitable network configurations to optimize their objectives. To minimize conflicts among the actions of multiple CFs, the CFs send their proposed configurations to a Controller, which in turn computes the final value that optimizes the combined interest of all the CFs. However, simulating real-life CAN deployments is challenging since: (a) the CFs have to reactively and interactively learn on the underlying system, and (b) the Controller must compute the optimal configurations in a dynamic environment with interdependent functions. In this letter, we present a scheme for implementing CAN in a simulation environment highlighting the critical design aspects for generating expected outcomes. Our results validate the proposed implementation design as the desired realistic behavior is obtained from CAN.